Semi-Automated Sleep EEG Scoring with Active Learning and HMM-Based Deletion of Ambiguous Instances

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00337775" target="_blank" >RIV/68407700:21730/19:00337775 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/proceedings2019031046" target="_blank" >https://doi.org/10.3390/proceedings2019031046</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/proceedings2019031046" target="_blank" >10.3390/proceedings2019031046</a>

Result language

angličtina

Original language name

Semi-Automated Sleep EEG Scoring with Active Learning and HMM-Based Deletion of Ambiguous Instances

Original language description

Sleep scoring is an important tool for physicians. Assigning of segments of long biomedical signal into sleep stages is, however, a very time consuming, tedious and expensive task which is performed by an expert. Automatic sleep scoring is not well accepted in clinical practice because of low interactivity and unacceptable error, which is often caused by inter-patient variability. This is solved by proposing a semi-automatic approach, where parts of the signal are selected for manual labeling by active learning and the resulting classifier is used for automatic labeling of the remaining signal. The active learning is disturbed by noisy ambiguous data instances caused by continuous character of the sleep stage transitions and a removal of such transitional instances from the training set prior to active learning can improve the efficiency of the method. This paper proposes to use the hidden Markov model for the detection of the transitional instances. It shows experimentally on 35 sleep EEG recordings that such a method significantly improves the semi-automatic method. A complete methodology for semi-automatic sleep scoring is proposed and evaluated, which can be better accepted as a decision support tool for sleep scoring experts.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA17-20480S" target="_blank" >GA17-20480S: Temporal context in analysis of long-term non-stationary multidimensional signal</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019

ISBN

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ISSN

2504-3900

e-ISSN

2504-3900

Number of pages

10

Pages from-to

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Publisher name

Multidisciplinary Digital Publishing Institute (MDPI AG)

Place of publication

Basel

Event location

Toledo

Event date

Dec 2, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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